MiniMax-01: Scaling Foundation Models with Lightning Attention
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2501.08313
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273
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MiniMax-01: Scaling Foundation Models with Lightning Attention
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Retrofitting (Large) Language Models with Dynamic Tokenization
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Byte Latent Transformer: Patches Scale Better Than Tokens
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euclaise's activity
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10
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2 days ago
Retrofitting (Large) Language Models with Dynamic Tokenization
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2411.18553
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2
Byte Latent Transformer: Patches Scale Better Than Tokens
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2412.09871
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91
Scalable-Softmax Is Superior for Attention
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2501.19399
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20
ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning
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2502.01100
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14
The Differences Between Direct Alignment Algorithms are a Blur
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2502.01237
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108
Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning
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2502.03275
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11
LIMO: Less is More for Reasoning
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2502.03387
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36
Demystifying Long Chain-of-Thought Reasoning in LLMs
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2502.03373
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39
BOLT: Bootstrap Long Chain-of-Thought in Language Models without Distillation
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2502.03860
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14
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6 days ago
Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps
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2501.09732
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67
Pairwise RM: Perform Best-of-N Sampling with Knockout Tournament
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2501.13007
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19
Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedback
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2501.12895
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55
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
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2501.12948
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305
Kimi k1.5: Scaling Reinforcement Learning with LLMs
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2501.12599
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90
Debate Helps Weak-to-Strong Generalization
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2501.13124
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7
Sigma: Differential Rescaling of Query, Key and Value for Efficient Language Models
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2501.13629
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43
RL + Transformer = A General-Purpose Problem Solver
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2501.14176
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22
ARWKV: Pretrain is not what we need, an RNN-Attention-Based Language Model Born from Transformer
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2501.15570
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23
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
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2501.17161
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101